Assessment of chemical‐crosslink‐assisted protein structure modeling in CASP13

Jorge Eduardo Fajardo (1, 2) , Rojan Shrestha (1, 2) , Nelson Gil (1, 2) , Adam Belsom (3) , Silvia Crivelli (4) , Cezary Czaplewski (5) , Krzysztof Fidelis (6) , Sergei Grudinin (7) , Mikhail Karasikov (8, 9, 10, 7) , Agnieszka Karczyńska (5) , Andriy Kryshtafovych (6) , Alexander Leitner (11) , Adam Liwo (5, 12) , Emilia Lubecka (13) , Bohdan Monastyrskyy (6) , Guillaume Pagès (7) , Juri Rappsilber (14, 2) , Adam Sieradzan (5) , Celina Sikorska (5) , Esben Trabjerg (11) , Andras Fiser (1, 2)

1 Department of Systems & Computational Biology [New York]
2 Department of Biochemistry [New York]
3 Institut für Biotechnologie [berlin]
4 CS - UC Davis - Department of Computer Science [Univ California Davis]
5 Department of Environmental Analytics [Univ Gdańsk]
6 Genome Center [UC Davis]
7 NANO-D-POST [2018-2020] - Algorithms for Modeling and Simulating Nanosystems [2018-...]
8 MIPT - Moscow Institute of Physics and Technology [Moscow]
9 Skoltech - Skolkovo Institute of Science and Technology [Moscow]
10 D-INFK - Department of Computer Science [ETH Zürich]
11 Institute of Molecular Systems Biology [Zurich]
12 KIAS - Korea Institute for Advanced Study
13 Institute of Mathematics [Univ Gdańsk]
14 Wellcome Trust Centre for Cell Biology

With the advance of experimental procedures obtaining chemical crosslinking information is becoming a fast and routine practice. Information on crosslinks can greatly enhance the accuracy of protein structure modeling. Here, we review the current state of the art in modeling protein structures with the assistance of experimentally determined chemical crosslinks within the framework of the 13th meeting of Critical Assessment of Structure Prediction approaches. This largest‐to‐date blind assessment reveals benefits of using data assistance in difficult to model protein structure prediction cases. However, in a broader context, it also suggests that with the unprecedented advance in accuracy to predict contacts in recent years, experimental crosslinks will be useful only if their specificity and accuracy further improved and they are better integrated into computational workflows.

Domaines: Bio-informatique [q-bio.QM]; Physique Numérique [physics.comp-ph]; Biophysique

Jorge Eduardo Fajardo, Rojan Shrestha, Nelson Gil, Adam Belsom, Silvia Crivelli, et al.. Assessment of chemical‐crosslink‐assisted protein structure modeling in CASP13. Proteins - Structure, Function and Bioinformatics, 2019, Critical Assessment of Methods of Protein Structure Prediction (CASP) Special Issue, 87 (12), pp.1283-1297. ⟨10.1002/prot.25816⟩. ⟨hal-02315542⟩